@article {bnh-8360, title = {Meteorological drivers of the eastern Victorian Black Summer (2019{\textendash}2020) fires}, journal = {Journal of Southern Hemisphere Earth Systems Science}, volume = {72}, year = {2022}, month = {09/2022}, pages = {139-163}, chapter = {139}, abstract = {

The spring and summer of 2019{\textendash}2020 (Black Summer) saw the largest and most significant bushfire outbreak recorded in eastern Australia. In Victoria, the fires ran from mid-November through early autumn. In this paper, we use a high-spatial and temporal resolution 48-year fire weather re-analysis data set (VicClim5) to describe fire weather and vertical wind and stability profiles for five significant high Forest Fire Danger Index (FFDI) fire events and compare these with detailed fire reconstructions. A feature of several of these fires was very active overnight fire spread driven by topographically enhanced low-level jets and low fine fuel moisture content. The FFDI values on these nights were either the highest or near highest on record in the 48-year data set. We describe cases of lightning ignition, prefrontal fire spread and two cases of post-frontal fire spread {\textendash} one into Mallacoota on the early morning of 31 December 2019 and the other a northward overnight run down the Buffalo Valley on 4{\textendash}5 January 2020. On two of the days studied there were complex wind changes associated with the inland penetration of low-level south-easterly winds under the influence of locally generated pressure gradients. An elevated hot, dry mixed layer above these shallow layers also played an important role. On one occasion there is some evidence of possible mountain-wave modulation of surface wind flows. These events demonstrate a range of features of the fire weather and climate in eastern Victoria and the utility of VicClim5 in 3-dimensional climatological analyses.

}, keywords = {Black Summer bushfires, climatology of extremes, cold fronts, eastern Victorian fire weather, elevated mixed layers, low level jets, overnight fire spread, wind changes}, doi = {https://doi.org/10.1071/ES22011}, url = {https://www.publish.csiro.au/ES/ES22011}, author = {Graham A. Mills and Owen Salkin and Matthew Fearon and Sarah Harris and Timothy Brown and Hauss Reinbold} } @article {bnh-7417, title = {Climatology of wind changes and elevated fire danger over Victoria, Australia}, journal = {Journal of Southern Hemisphere Earth System Science}, year = {2020}, month = {10/2020}, abstract = {

Wind changes are a critical factor in fire management, particularly on days of elevated fire danger, and have been shown to be a factor in many firefighter entrapments in Australia and the USA. While there have been numerous studies of frontal wind changes over southeastern Australia since the 1950s, a spatial climatology of wind change strength and frequency over Victoria has hitherto been limited by the relatively low number of observation sites that have both high temporal resolution observations and sufficient length of record. This study used a recently developed high spatial (4-km grid) and temporal (1 hour) resolution, 46-year, homogeneous gridded fire weather climatology data set to generate a climatology of wind change strength by season at each gridpoint across Victoria. The metric used to define a wind change is the vector difference between the wind speed and direction over each 1-hour interval, with the highest value occuring on each day being selected for spatial analysis of strength and frequency. The highest values of wind change strength are found along the crest of the Great Dividing Range (the Great Divide), with a peak in spring. Elsewhere, the highest values occur in summer, with the areas south of the Great Divide, west of Melbourne and in central Gippsland showing higher values than the remainder of the state. The strength of wind changes generally decreases north of the Great Divide, although it is stronger in the northwest of the state in spring rather than in autumn. Lowest summertime (and other seasons) values occur in the northeast of the state and in far-east Gippsland. Exploring the frequencies of days when the highest daily Forest Fire Danger Index and the highest daily wind change strength jointly exceed defined thresholds shows that the northwest of the state has the highest springtime frequencies, whereas the highest autumn frequencies occur west of Melbourne and south of the Great Divide. The highest numbers of joint events in summer (when the greatest frequencies also occur) extend from central Victoria west to the South Australian border, with a secondary maximum in central Gippsland. These analyses offer important information for fire weather forecasters and for fire practitioners when preparing for a fire season or managing a fire campaign (for example, for allocating resources or understanding risks).

}, keywords = {Australia, bushfires, climatology, extreme weather, fire danger, fire management, firefighter entrapment, Victoria, wind changes}, doi = {https://doi.org/10.1071/ES19043}, url = {https://www.publish.csiro.au/es/ES19043}, author = {Graham A. Mills and Sarah Harris and Brown, T and Alex Chen} } @article {bnh-6167, title = {Understanding the variability of Australian fire weather between 1973 and 2017}, journal = {PLOS ONE}, volume = {14}, year = {2019}, month = {09/2019}, abstract = {

Australian fire weather shows spatiotemporal variability on interannual and multi-decadal time scales. We investigate the climate factors that drive this variability using 39 station-based historical time series of the seasonal 90th-percentile of the McArthur Forest Fire Danger Index (FFDI) extending from 1973 through 2017. Using correlation analyses, we examine the relationship of these time series to the El Ni{\~n}o Southern Oscillation (ENSO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD), considering both concurrent and time-lagged relationships. Additionally, longer term behaviour of the time series using linear trend analysis is discussed in the context of the climate drivers, Interdecadal Pacific Oscillation (IPO) and anthropogenic climate change. The results show that ENSO is the main driver for interannual variability of fire weather, as defined by FFDI in this study, for most of Australia. In general, El Ni{\~n}o-like conditions lead to more extreme fire weather, with this effect stronger in eastern Australia. However, there are significant regional variations to this general rule. In NSW, particularly along the central coast, negative SAM is a primary influence for elevated fire weather in late-winter and spring. In the southeast (VIC and TAS), the El Ni{\~n}o-like impact is exacerbated when positive IOD conditions are simultaneously observed. The spring conditions are key, and strongly influence what is observed during the following summer. On longer time scales (45 years), linear trends are upward at most stations; this trend is strongest in the southeast and during the spring. The positive trends are not driven by the trends in the climate drivers and they are not consistent with hypothesized impacts of the IPO, either before or after its late-1990s shift to the cold phase. We propose that anthropogenic climate change is the primary driver of the trend, through both higher mean temperatures and potentially through associated shifts in large-scale rainfall patterns. Variations from interannual factors are generally larger in magnitude than the trend effects observed to date.

}, keywords = {fire weather; variability; climate drivers; climate change}, doi = {https://doi.org/10.1371/journal.pone.0222328}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222328\&ct=t(EMAIL_CAMPAIGN_10_29_2019_23_20)\&mc_cid=831ea64e9e\&mc_eid=73e0665bda}, author = {Sarah Harris} } @article {bnh-5264, title = {Determining the minimum sampling frequency for ground measurements of burn severity}, journal = {International Journal of Wildland Fire}, year = {2018}, month = {06/2018}, abstract = {

Understanding burn severity is essential to provide an overview of the precursory conditions leading to fires as well as understanding the constraints placed on fire management services when mitigating their effects. Determining the minimum sampling frequency for ground measurements is not only essential for accurately assessing burn severity, but also for fire managers to better allocate resources and reduce the time and costs associated with sampling. In this study, field sampling methods for assessing burn severity are analysed statistically for 10 burn sites across Victoria, Australia, with varying spatial extents, topography and vegetation. Random and transect sampling methods are compared against each other using a Monte Carlo simulation to determine the minimum sample size needed for a difference of 0.02 (2\%) in the severity classes proportions relative to the population proportions. We show that, on average, transect sampling requires a sampling rate of 3.16\% compared with 0.59\% for random sampling. We also find that sites smaller than 400 ha require a sampling rate of between 1.4 and 2.8 times that of sites larger than 400 ha to achieve the same error. The information obtained from this study will assist fire managers to better allocate resources for assessing burn severity.

}, doi = {10.1071/WF17055}, url = {http://www.publish.csiro.au/WF/WF17055}, author = {Alexander Holmes and Sarah Harris and Nigel Tapper and Christoph R{\"u}diger} } @article {bnh-3919, title = {Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data}, journal = {Environmental Modelling \& Software}, volume = {97}, year = {2017}, month = {11/2017}, pages = {61-71}, chapter = {61}, abstract = {

Accurate description of forest surface fuel load is important for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, assessing potential fire hazards and assisting in fuel hazard-reduction burns to reduce fire risks to the community and the environment. Bushfire related studies and current operational activities have a common challenge in quantifying fuels, since the fuel load varies across the landscape. This paper developed a predictive model that efficiently and accurately estimates quantities of surface fuel in Australian southeast Eucalypt forests. Model coefficients were determined through a three-step process that attempts to evaluate how the spatial variation in surface fuel load relates to litter-bed depth, fuel characteristics, topography and previous fire disturbance. First, the forest surface fuel depth-to-load relationship was established; second, key quantitative variables of environmental factors were added; and third, important qualitative variables of fuel characteristics were included. The verification of model prediction was conducted through leave-one-out cross-validation (CV). Light Detection and Ranging was used to quantify forest structural characteristics and terrain features. The calibrated model had a\ R2\ of 0.89 (RMSE\ =\ 20.7\ g) and performed better than the currently used surface fuel load models, including McArthur{\textquoteright}s (R2\ =\ 0.61 and\ RMSE\ =\ 39.6\ g) and Gilroy and Tran{\textquoteright}s (R2\ =\ 0.69 and\ RMSE\ =\ 36.5\ g) models. This study describes a novel approach to forest surface fuel load modelling using forest characteristics and environmental factors derived from LiDAR data through statistical analysis. The model established in this study can be used as an efficient approach to assist in forest fuel management and fire related operational activities.

}, doi = {10.1016/j.envsoft.2017.07.007}, url = {http://www.sciencedirect.com/science/article/pii/S1364815216304418}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @conference {bnh-3232, title = {Estimation of forest surface fuel load using airborne LiDAR data}, booktitle = {SPIE Remote Sensing}, year = {2017}, month = {09/2017}, publisher = {SPIE}, organization = {SPIE}, address = {Warsaw, Poland}, abstract = {

Accurately describing forest surface fuel load is significant for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, as well as assessing potential fire hazards. In this study, the Light Detection and Ranging (LiDAR) data was used to estimate surface fuel load, due to its ability to provide threedimensional
information to quantify forest structural characteristics with high spatial accuracies. Firstly, the multilayered eucalypt forest vegetation was stratified by identifying the cut point of the mixture distribution of LiDAR point density through a non-parametric fitting strategy as well as derivative functions. Secondly, the LiDAR indices of heights, intensity, topography, and canopy density were extracted. Thirdly, these LiDAR indices, forest type and previous fire disturbances were then used to develop two predictive models to estimate surface fuel load through multiple regression analysis. Model 1 was developed based on LiDAR indices, which produced a R2 value of 0.63. Model 2 (R2 = 0.8) wasderived from LiDAR indices, forest type and previous fire disturbances. The accurate and consistent spatial variation in surface fuel load derived from both models could be used to assist fire authorities in guiding fire hazard-reduction burns and fire suppressions in the Upper Yarra Reservoir area, Victoria, Australia.

}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @conference {bnh-2074, title = {Victoria fire weather climatology dataset - peer viewed}, booktitle = {Adelaide Conference 2015}, year = {2015}, address = {Adelaide, Australia}, abstract = {

Research proceedings from the Bushfire and Natural Hazards CRC \& AFAC Conference in Adelaide, 1-3 September 2015.\ 

}, author = {Sarah Harris and Graham A. Mills and Brown, T and Domagoj Podmar and H. Reinbold and Matt Fearon} } @article {BF-3183, title = {The relationship between fire behaviour measures and community loss: an exploratory analysis for developing a bushfire severity scale}, journal = {Natural Hazards}, year = {2012}, abstract = {Current fire danger scales do not adequately reflect the potential destructive force of a bushfire in Australia and, therefore, do not provide fire prone communities with an adequate warning for the potential loss of human life and property. To determine options for developing a bushfire severity scale based on community impact and whether a link exists between the energy release rate (power) of a fire and community loss, this paper reviewed observations of 79 wildfires (from 1939 to 2009) across Victoria and other southern states of Australia. A methodology for estimating fire power based on fuel loading, fire size and progression rate is presented. McArthur{\textquoteright}s existing fire danger indices (FDIs) as well as fuel- and slope-adjusted FDIs were calculated using fire weather data. Analysis of possible relationships between fire power, FDIs, rate of spread and Byram{\textquoteright}s fireline intensity and community loss was performed using exposure as a covariate. Preliminary results showed that a stronger relationship exists between community loss and the power of the fire than between loss and FDI, although fuel-adjusted FDI was also a good predictor of loss. The database developed for this study and the relationships established are essential for undertaking future studies that require observations of past fire behaviour and losses and also to form the basis of developing a new severity scale}, issn = {0921-030X}, doi = {10.1007/s11069-012-0156-y}, author = {Sarah Harris and Wendy R. Anderson and Kilinc, Musa and Fogarty, Liam} }